In today's society, end users regularly utilize smartphones, speech recognition systems, and other audio-based technologies to place and receive phone calls, access various types of internet services, and perform a variety of functions, or a combination thereof. When an end user communicates using such devices and technologies, the end user may be located in an environment that may not provide for optimal communication conditions. Such environments may include ambient noise, which may pose substantial challenges when end users attempt to communicate with others or with automatic speech recognition systems. In recent years, these challenges have only been exacerbated based on a number of trends. In particular, there has been a large migration away from traditional handset designs towards rectangular-shaped smartphones, which are inferior to traditional handset designs at coupling a talker's voice to the microphone of the smartphone. Additionally, an increasing number of end users are using speakerphones on both wired and wireless platforms, which admit substantial amounts of background noise relative to the talker's voice. Furthermore, there has been an increasing number of end users that are using smartphones in noisy environments, a greater societal acceptance of taking calls in the presence of others engaged in noisy activities, and an increasing use of automated speech recognition systems to interact with local and network resources.
In order to counteract the ambient noise existing in an end user's environment, traditional solutions typically involve brute-force processing, in isolation, of all of the various audio information occurring in the environment. Such brute-force processing often requires extensive use of limited network resources, causes communication delays, increases power usage, and increases network and other costs. While some currently existing solutions utilize noise suppression algorithms and technologies to separate a selected end user's audio signals from the ambient noise in the environment, such noise suppression algorithms and technologies often do so only by predefining the locations of the sources of the ambient noise and the location of the selected end user.